DHA Service Access and Coverage

access
spatial
DHA
registration
Analysis of population coverage and accessibility of Department of Home Affairs offices across South Africa
Published

January 20, 2026

1 Overview

Birth and death registration may be incomplete due to the inaccessibility of Home Affairs offices where registration occurs. A potential barrier to registration may be the distance needed to travel. We aim to determine the proportion of the population within reasonable distances of Home Affairs offices.

2 Methodology

2.1 Data Sources

  • Shape files: District boundaries from Stats SA, standardised using the {NMCleaner} package
  • Population estimates: Stats SA mid-year population estimates (2025), at district level
  • Office locations: DHA office coordinates from existing dataset (excludes hospital satellite offices)

2.2 Analytical Approach

We explored three approaches to estimate population coverage:

  1. Office-to-office distance: Measured median half-distance between nearest office pairs. This underestimated coverage in rural areas.

  2. Uniform grid distribution: Created 1km grid and distributed population uniformly within districts. Ignored population clustering.

  3. Inverse power model (selected): Assumed population clusters around offices with distance decay.

2.3 Population Density Models

Inverse Power Model: \[ w_i = \frac{1}{(d_i + 1)^{\alpha}} \]

where \(d_i\) is the distance (km) from grid point \(i\) to the nearest office, and \(\alpha = 1.5\) is the decay parameter.

Population Allocation:

Weights are normalised within each district: \[ \hat{w}_i = \frac{w_i}{\sum_{j \in d} w_j} \]

The population at each grid point is: \[ P_i = P_d \cdot \hat{w}_i \]

where \(P_d\) is the total population of district \(d\).

2.4 Limitations

  • Population is modelled, not observed at sub-district level
  • Distances are straight-line, not road-network or travel-time
  • Office capacity and service quality are not accounted for
  • Satellite offices (e.g., in hospitals) are not included

3 National Overview Maps

3.1 Population Distribution by District

3.2 Number of DHA Offices by District

3.3 Modelled Population Distribution

4 Coverage Summary

Table 1

District

Total Population

Population within 10 km

% Population within 10 km

Population within 20 km

% Population within 20 km

Median Distance to Nearest DHA Office (km)

Alfred Nzo

935,303

455,654

49

743,056

79

10

Amajuba

610,841

248,305

41

412,243

67

7

Amathole

792,612

384,504

49

624,102

79

14

Bojanala Platinum

1,985,081

973,231

49

1,498,577

75

7

Buffalo City

0

0

0

7

Cape Winelands

1,014,432

393,345

39

660,490

65

22

Capricorn

1,412,657

704,095

50

1,090,472

77

7

Central Karoo

77,157

19,338

25

33,406

43

61

Chris Hani

717,289

284,460

40

477,557

67

19

City of Cape Town

5,030,497

4,099,372

81

4,680,809

93

6

City of Johannesburg

5,900,321

5,628,983

95

5,892,801

100

5

City of Tshwane

4,038,061

2,807,261

70

3,797,729

94

6

Dr Kenneth Kaunda

807,057

306,766

38

534,494

66

26

Dr Ruth Segomotsi Mompati

474,901

126,707

27

226,657

48

37

Ehlanzeni

1,928,692

949,462

49

1,514,775

79

15

Ekurhuleni

4,059,057

3,606,629

89

4,026,309

99

4

Fezile Dabi

536,755

210,022

39

354,982

66

26

Frances Baard

438,829

252,122

57

344,875

79

7

Garden Route

673,192

231,969

34

359,567

53

26

Gert Sibande

1,367,513

499,243

37

865,389

63

24

Harry Gwala

507,708

244,652

48

393,555

78

15

Joe Gqabi

354,931

120,698

34

201,178

57

26

John Taolo Gaetsewe

296,434

60,904

21

105,422

36

59

King Cetshwayo

992,551

584,675

59

878,508

89

11

Lejweleputswa

698,356

237,433

34

415,209

59

22

Mangaung

857,973

368,652

43

569,402

66

7

Mopani

1,266,834

637,249

50

965,505

76

11

Namakwa

129,515

18,623

14

32,228

25

102

Nelson Mandela Bay

1,263,632

913,990

72

1,177,353

93

6

Ngaka Modiri Molema

916,907

356,377

39

585,529

64

11

Nkangala

1,779,928

915,035

51

1,379,990

78

15

O.R. Tambo

1,623,984

901,159

55

1,383,467

85

11

Overberg

329,835

129,159

39

214,445

65

30

Pixley ka Seme

219,155

44,535

20

78,796

36

61

Sarah Baartman

527,418

137,390

26

236,924

45

36

Sedibeng

1,061,185

698,345

66

956,982

90

5

Sekhukhune

1,333,432

623,524

47

1,030,150

77

14

Thabo Mofutsanyana

810,097

281,906

35

474,807

59

21

Ugu

831,709

410,976

49

648,175

78

16

Umzinyathi

607,975

279,573

46

481,574

79

20

Vhembe

1,527,097

814,784

53

1,210,958

79

11

Waterberg

826,172

270,753

33

469,023

57

20

West Coast

502,576

158,658

32

266,241

53

54

West Rand

1,046,310

595,362

57

881,713

84

7

Xhariep

136,652

32,346

24

58,520

43

33

ZF Mgcawu

295,250

65,365

22

109,046

37

50

Zululand

901,275

416,978

46

696,859

77

14

eThekwini

4,374,202

3,566,221

82

4,314,339

99

4

iLembe

742,038

487,512

66

715,374

96

14

uMgungundlovu

1,220,477

558,333

46

974,950

80

18

uMkhanyakude

711,366

369,938

52

571,470

80

20

uThukela

732,104

233,311

32

401,014

55

29

Total

62,225,326

37,715,880

61

51,016,996

82

14

5 Interactive Map

Explore DHA office locations with coverage buffers (10km, 20km, 50km).

6 Key Findings

ImportantSummary Statistics
  • 61% of the population lives within 10km of a DHA office
  • 82% of the population lives within 20km of a DHA office
  • Rural districts show significantly lower coverage

7 Recommendations

  1. Request enumeration area data: Census data at smaller geographic units would improve population distribution estimates
  2. Include satellite offices: Hospital-based registration points should be mapped
  3. Road network analysis: Travel time may be more relevant than straight-line distance